modified_googlenet.py 文件源码

def __call__(self, x, subtract_mean=True):
        if subtract_mean:
            x = x - self._image_mean
#        h = super(ModifiedGoogLeNet, self).__call__(
#            x, layers=['pool5'], train=train)['pool5']
#        h = self.bn_fc(h, test=not train)
#        y = self.fc(h)
#        return y
        h = F.relu(self.conv1(x))
        h = F.max_pooling_2d(h, 3, stride=2)
        h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4/5)
        h = F.relu(self.conv2_reduce(h))
        h = F.relu(self.conv2(h))
        h = F.local_response_normalization(h, n=5, k=1, alpha=1e-4/5)
        h = F.max_pooling_2d(h, 3, stride=2)
        h = self.inc3a(h)
        h = self.inc3b(h)
        h = F.max_pooling_2d(h, 3, stride=2)
        h = self.inc4a(h)
        h = self.inc4b(h)
        h = self.inc4c(h)
        h = self.inc4d(h)
        h = self.inc4e(h)
        h = F.max_pooling_2d(h, 3, stride=2)
        h = self.inc5a(h)
        h = self.inc5b(h)
        h = F.average_pooling_2d(h, 7, stride=1)
        h = self.bn_fc(h)
        y = self.fc(h)
        if self.normalize_output:
            y = F.normalize(y)
        return y